Apabetalone: Can be repurposed or not against SARS-CoV-2?
Shristi Singhania
Amity Institute of Biotechnology, Amity University Uttar Pradesh, India.
#Corresponding Author Email ID: kamal.rawal@gmail.com
Centre for Computational Biology and Bioinformatics,
AIB Amity University, Noida
Website: http://drugx.kamalrawal.in/drugx/
Abstract
Background: Since December 2019, the world has been dealing with the pandemic COVID-19, which is caused by the severe acute respiratory syndrome coronavirus 2. (SARS-CoV-2). COVID-19 has been identified as a complicated disease that causes significant respiratory pathology as well as various extrapulmonary signs. Effective medicines are urgently needed to prevent infection and improve outcomes for COVID-19 patients. The pharmaceutical sector is working hard to discover novel COVID-19 treatments. Molnupiravir, an orally active RdRp inhibitor, is being tested against COVID-19 in a phase 3 clinical trial. Manipulation of epigenetic machinery to alter viral infectivity of host cells is a topic that has received little attention. It has been shown that the bromodomain and extra terminal (BET) family of epigenetic readers modulates SARS-CoV-2 infection.
Methods: The CoV-DrugX pipeline was implemented to observe if the drug apabetalone could be used as a repurposed drug against Covid-19. Apabetalone is a well-tolerated BET protein inhibitor in late-stage clinical research for cardiovascular disease that does not increase blood pressure. This research looks into the possibility of repurposing apabetalone to lower CoV-2 infection in the lung and other tissues by downregulating ACE2 expression.
Result and Conclusion: As a result, we thoroughly compared the properties of apabetalone to those of COVID-19 using our DrugX database. All of the most recent forecast results are aggregated and displayed in one place.
Keywords: Covid-19, SARS-CoV-2, Epigenetic machinery, BET protein inhibitor, CoV-DrugX pipeline, Apabetalone, Repurposed,
Introduction
The new coronavirus disease of 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a pandemic that has killed over 2 million people worldwide and remains a public health disaster [Soy M et al., 2020]. Vaccines have changed the course of the pandemic, but there is still a great demand for medications that can treat Covid [Pardi N et al., 2018]. Vaccine immunity can decrease and access to vaccines remains a serious issue around the world. In addition, emergence of the new variants emphasises the potential necessity for a backup. There are now many drugs that target the virus or our bodies in various ways [WHO]. One such drug that we’ll discuss in this paper is apabetalone (See Figure 1).
Apabetalone is the global leader in a new class of medications that modulate disease-associated proteins [McLure KG et al., 2013]. Traditional treatments aim to treat or cure complicated and chronic disorders by targeting a single disease-causing component. Apabetalone positively influences many disease-causing biological processes by blocking the action of bromodomain and extra terminal domain (BET) proteins, ushering in a new paradigm shift in the treatment of chronic disease [Shishikura D et al., 2019]. Apabetalone has a distinct mode of action that operates at the level of "transcription" [Ray KK et al., 2020]. It acts as a selective inhibitor of the BET family of proteins and has the ability to correct dysregulated epigenetics, which are at the root of many chronic disorders. Developed by Resverlogix Corp., Apabetalone is an orally accessible small molecule that is being tested in clinical studies for the treatment of arthrosclerosis and related cardiovascular disease [McNeill E 2010].
Implementation
CoV-DrugX software pipeline is a Machine Learning based Drug repurposing pipeline against SARS-CoV-2 and Human proteome. The pipeline implements eight network medicine drug repurposing modules to shortlist probable drugs against COVID-19 on the basis of the scores gained by a drug in each module. For example, in the Drug Target module, if a drug candidate’s target molecule matches with the target molecules of COVID-19, then we assign a score of 1 to the drug candidate. Each module has a distinct pattern of scoring the drug candidate. The cumulative score of all the modules is the sum of their respective scores.
The modules are:
1 & 2: Deep learning (DL) based modules that compute 11 biological properties such as mutagenicity and drug likeness, and 200 cheminformatic properties such as logP value and molecular weight of drug candidates respectively.
3: DrugDock module which uses AutoDock Vina to carry out molecular docking of proteins with specific ligands (drug candidates) [Clark DE et al., 2000].
4: Drug Target module scores a drug candidate on the basis of the similarity of their target molecules to the target molecules of drugs associated with COVID-19. If there’s a match between the target molecules of the query drug and the target molecules of COVID-19, then the module predicts a score of 1, otherwise 0.
5: The Drug Side-effect module scores a drug candidate on the basis of the similarity of their side effects to the side effects of drugs associated with COVID-19. Study of adverse drug reactions (ADR) is vital for the drugs to be used in the treatment of COVID-19 [Aygün İ et al., 2020].
6: Gene expression- After the initial evaluation and identification of lead molecules, gene expression profiling and bioinformatics analysis would be particularly important to gain insights in gene expression patterns. The module takes the name of the drug as input query and parses through the drug-gene interaction dataset (DGIdb) to find the gene(s) that interact with the query drug. The expression of these genes, whether upregulated or downregulated, is searched in the database of DEGs extracted from Blanco et al., 2020 and MSigDB.
7: Drug Phenotype Module provides COVID-19 phenotypes for a drug.
8: Drug-Gene Network- In this module, we assembled the interactions of genes, proteins and drugs that are associated with COVID-19 from the literature using text mining and deep curation approaches.
Supplementary Information of the pipeline: https://sites.google.com/view/drugx-supplementary
Usage
Each of the modules takes the input of the drug candidate in SMILES format and predicts whether the candidate could be repurposed against COVID-19. The SMILES format of the respective drug (here apabetalone) is extracted from DrugBank, saved in .txt format and then put as the input in the CoV-DrugX pipeline (http://drugx.kamalrawal.in/drugx/). The scores are produced in the range 0 and 1 where 0 refers to the drug not to be considered in drug repositioning for COVID-19, while 1 symbolizes that the drug should be considered into drug repurposing for COVID-19. In addition, intermediate files for each module are generated, offering more functional and elaborate information contained in the module associated to the input drug.
Results
The modules are based on multiple computational drug repurposing methodologies. There are two modules that determine whether a medicine is COVID19 repurposable based on 11 biological features (See Table 2) and 200 cheminformatics properties of the SARS COVID-19 virus (See Table 3). Then, we have a set of modules that analyse medications based on COVID19-associated diseases, phenotypes identified in COVID19, drugs with symptoms comparable to COVID19, drug side effects connected to curing and preventing COVID19, and a target, gene expression-based method. Another strategy employed in our pipeline is based on docking. Three such modules are constructed, one for human targets, one for viral proteins, and one for protein kinases connected with human proteins as targets for the query drug. In addition to this, we have included a module for determining the Euclidean distance between the medicine and the COV-2 disease, as well as a module for checking the query drug if it has any association with the virus.
Out of the 14 modules in total, it was observed that the database had results for only 7 modules (as these modules showed a score of 1, whereas others showed 0). These four modules were B, D, G, H, I and O (See Table 1).
The common COVID-19 phenotypes seen in the drug was AAPOAI AMYLOIDOSIS (See Table 5). Apabetalone seemed to upregulate and downregulate the gene BRD4 and downregulate the gene APOA1 (See Table 4). The SI score was 5.5 and the PI score 0.40.
Apabetalone when docked against 23 viral proteins, using drug_dock_viral, results are interpreted in the form of Binding Affinities (which shows the strength of binding interaction). The average binding affinity came out to be -6.8 KCal/mol (See Table 7). Amongst all the ones with the highest interacting viral proteins with Apabetalone are Nsp2, Nsp4, Nsp13 and S_trimer.
Similarly, Table 6 shows the average binding energy of Apabetalone when docked against human proteins (ACE2, TMPRSS2) and their associated protein kinases (AAK1 and JAK1/2) to be -7.67 kcal/mol.
The total number of modules having data related to Apabetalone were 7 showing a binary score of 1.
So, the average score came (= No. of modules giving score 1/ Total number of modules) out to be 50%. Hence, based upon the average score, we can say that there is a 50% probability for the drug to be considered for repurposing against COVID-19.
Conclusion
The CoV-DrugX pipeline, written in HTML, provides a user-friendly interface that helps study the various properties of drugs (in SMILE format) against COVID-19. It consists of different modules that scores the candidate drug accordingly. Since, there is no such solid evidence or data yet according to the results derived from the pipeline, regarding the activity of Apabetalone against the SarsCoV-2, we can say that the drug stands a chance of 50% of being used as a repurposed drug in the treatment of COVID-19.
References
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Contribution of Authors
This study was conducted under the overall guidance of Dr. Kamal Rawal, who contributed in protocol, critical evaluation of data and manuscript. The pipeline was designed, constructed and validated by Robin Sinha and Prashant Singh. The editing was done by Sweety Dattatraya Shinde and Ridhima.
Acknowledgement
I extend my sincere gratitude to Amity University for providing administrative and technical support required in the conduct of this study.